Apriori and FP-Growth Comparative Analysis of MPL Indonesia Season 13 Hero Drafts
DOI:
https://doi.org/10.69533/informatech.volume3number1.524Keywords:
Association Rule Mining, Apriori, FP-Growth, MPL Indonesia, Winning Patterns.Abstract
Hero combination selection or drafting is a crucial factor in determining victory in Mobile Legends: Bang Bang (MLBB) games at the professional level such as MPL Indonesia Season 13. However, counter-pick strategies are often based solely on the subjective intuition of players or coaches. This study aims to provide an objective basis for determining winning hero combination patterns by applying the Association Rule Mining (ARM) technique. Two main algorithms, namely Apriori and Frequent Pattern Growth (FP-Growth), are compared to evaluate the performance efficiency and accuracy of the resulting rules. The research data includes 183 winning transactions during the regular season of MPL ID Season 13, with parameters of minimum support 0.05 (5%), minimum confidence 0.40 (40%), and minimum lift 1.2. The results show that the strongest association rules are found in the combinations {Lapu-lapu} → {Fredrinn} (confidence 0.71) and {Cici} → {Fredrinn} (confidence 0.59). In terms of technical performance, the Apriori algorithm recorded a faster execution time than FP-Growth on this dataset. This study concluded that both algorithms produce identical association rule outputs, while Apriori demonstrated faster execution on this small-scale dataset, a finding attributed to the limited transaction volume rather than a universal superiority of Apriori over FP-Growth. The resulting rules can serve as a data-driven strategic recommendation system for professional esports teams in the pick and ban phase.
Downloads
References
Moonton Technology. (2024). Mobile Legends: Bang Bang — about us. [Online]. Available: https://en.moonton.com/about/index.html
S. Liu, R. Ma, C. Zhao, Z. Li, J. Xiao, and Q. Li, “BPCoach: Exploring hero drafting in professional MOBA tournaments via visual analytics,” Proc. ACM Hum.-Comput. Interact., vol. 8, no. CSCW1, Art. no. 200, Apr. 2024, doi: 10.1145/3637303.
Z. Chen, T.-H. D. Nguyen, Y. Xu, C. Amato, S. Cooper, Y. Sun, and M. S. El-Nasr, “The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games,” in Proc. 12th ACM Conf. Recommender Syst. (RecSys ’18), Vancouver, BC, Canada, Oct. 2018, pp. 200–208, doi: 10.1145/3240323.3240345.
R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, Washington, DC, USA, May 1993, pp. 207–216, doi: 10.1145/170035.170072.
V. Srinadh, “Evaluation of Apriori, FP-Growth and Eclat association rule mining algorithms,” Int. J. Health Sci., vol. 6, no. S2, pp. 7475–7485, 2022, doi: 10.53730/ijhs.v6nS2.6729.
R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th Int. Conf. Very Large Data Bases (VLDB), Santiago, Chile, Sep. 1994, pp. 487–499. [Online]. Available: https://www.vldb.org/conf/1994/P487.PDF
J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, Dallas, TX, USA, May 2000, pp. 1–12, doi: 10.1145/342009.335372.
Y. Shen, J. Zhou, W. Lin, and Z. Feng, “A deep learning supported sequential recommendation mechanism for ban-pick in MOBA games,” in Proc. 2022 IEEE Int. Conf. Softw. Eng. Artif. Intell. (SEAI), Guangzhou, China, Jun. 2022, pp. 259–265, doi: 10.1109/SEAI55746.2022.9832346.
V. Araujo, F. Rios, and D. Parra, “Data mining for item recommendation in MOBA games,” Proc. 13th ACM Conf. Recommender Syst. (RecSys '19), Copenhagen, Denmark, Sep. 2019, pp. 393–397. DOI: https://doi.org/10.1145/3298689.3346986
D. Alcan, K. Ozdemir, B. Ozkan, A. Y. Mucan, and T. Ozcan, “A comparative analysis of Apriori and FP-Growth algorithms for market basket analysis using multi-level association rule mining,” in Lecture Notes in Management and Industrial Engineering, Cham, Switzerland: Springer, 2023, pp. 131–142, doi: 10.1007/978-3-031-25847-3_13.
Z. Yang, Z. Pan, Y. Wang, D. Cai, S. Shi, S.-L. Huang, W. Bi, and X. Liu, “Interpretable real-time win prediction for Honor of Kings — a popular mobile MOBA esport,” IEEE Trans. Games, vol. 14, no. 4, pp. 589–597, Dec. 2022, doi: 10.1109/TG.2022.3149044.
S. Raschka, “MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack,” J. Open Source Softw., vol. 3, no. 24, p. 638, 2018. [Online]. Available: https://joss.theoj.org/papers/10.21105/joss.00638
S. Kotsiantis and D. Kanellopoulos, “Association rules mining: a recent overview,” GESTS Int. Trans. Comput. Sci. Eng., vol. 32, no. 1, pp. 71–82, 2006.
M. J. Zaki, “Scalable algorithms for association mining,” IEEE Trans. Knowl. Data Eng., vol. 12, no. 3, pp. 372–390, May/Jun. 2000. [Online]. Available: https://ieeexplore.ieee.org/document/846291
O. Delalleau, S. Malo, G. Gaudette, and S. Lazaridis, “Beyond the meta: leveraging game design parameters for patch-agnostic esport analytics,” in Proc. AAAI Workshop AI Esports, Washington, DC, USA, Feb. 2023. [Online]. Available: https://www.researchgate.net/publication/374517745
D. W. Cheung, J. Han, V. T. Ng, and C. Y. Wong, “Maintenance of discovered association rules in large databases: an incremental updating technique,” in Proc. 12th IEEE Int. Conf. Data Eng. (ICDE), New Orleans, LA, USA, Feb. 1996, pp. 106–114.
A. M. R. Putri and M. Kuswinarno, “Pengembangan e-sport di Indonesia, peluang, tantangan, dan masa depan industri: studi kasus Mobile Legends Professional League (MPL),” Jurnal Media Akademik, 2024. [Online].
S. Mohamed, “AI and Blockchain in Cybersecurity: A Sustainable Approach to Protecting Digital Assets,” J. Ilm. Inform. dan Komput., vol. 2, no. 1, pp. 1–8, 2025.
M. S. Rumetna, T. N. Lina, J. Karay, A. B. Santoso, and W. Ferdinandus, “Application of the Hungarian Algorithm for Workforce Task Optimization in Mobile Device Repair Operations,” J. Ilm. Inform. dan Komput., vol. 2, no. 2, pp. 94–101, 2025.
R. Lin, S. Lu, A. Yang, W. Shen, and J. Ren, “Multi-criteria sustainability assessment and decision-making framework for hydrogen pathways prioritization: An extended ELECTRE method under hybrid information,” Int. J. Hydrogen Energy, vol. 46, no. 24, pp. 13430–13445, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Andre Immanuel Porayou, Jacob Alfanicolls Rahayaan, Ade Yusupa, Yaulie Deo Y. Rindengan (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
